To estimate accurately the parameters of a regression model, the sample size must be large enough relative to the number of possible predictors for the model. In practice, sufficient data is often lacking, which can lead to overfitting of the model and, as a consequence, unreliable predictions of the outcome of new patients. Pooling data from different data sets collected in different (medical) centers would alleviate this problem, but is often not feasible due to privacy regulation or logistic problems. An alternative route would be to analyze the local data in the centers separately and combine the statistical inference results with the Bayesian Federated Inference (BFI) methodology. The aim of this approach is to compute from the inference results in separate centers what would have been found if the statistical analysis was performed on the combined data. We explain the methodology under homogeneity and heterogeneity across the populations in the separate centers, and give real life examples for better understanding. Excellent performance of the proposed methodology is shown. An R-package to do all the calculations has been developed and is illustrated in this article. The mathematical details are given in the Appendix.

Steringotrema microacetabularis Suriano & Martorelli, 1983 (Fellodistomidae) was described from the flounder Paralichthys orbignyanus. Later, it was redescribed, based on new material from the same host and type locality, and reconsidered as Bacciger microacetabularis (Baccigeridae). The main difference noted in the redescription was the presence of spines on the body. However, the lack of DNA data made confirming the true affiliation of this digenean challenging. New specimens sampled from P. orbignyanus allowed us to sequence the 28S, ITS, and COI genes. Fresh specimens were stained to compare their morphology with the holotype and voucher specimens. The digeneans found correspond with those reported from Mar Chiquita, described as B. microacetabularis (=S. microacetabularis). Genetic analyses clustered the newly sequenced individuals within the Cryptogonimidae, showing relationships with Oligogonotylus manteri, Tabascotrema verai, and Caecincola parvulus (28S); T. verai, Lobosorchis spp., Euryakaina manilensis, and Metadena marina (ITS); and Siphoderina spp. (COI). After comparing the species with cryptogonimids lacking spines in the oral sucker, a new genus, Surianotrema n. gen., is described. This genus increases the number of cryptogonimid genera known in South America to seven – three in freshwater and four in marine environments – with Surianotrema n. gen. being the first to be sequenced. New sequences from other South American cryptogonimids are necessary to better understand the phylogenetic relationships between genera within this family, particularly in South America.

This paper focuses on two phenomena in Irish agreement – namely, complementarity between overt in-situ arguments and agreement, and the obviation of this complementarity under A-movement. An analysis of these facts is offered in terms of the defective goal ‘incorporation’ (DGI) mechanism proposed by Roberts (2010), and applied to cases of complementarity in Bantu languages by Iorio (2014), and van der Wal (2015, 2020, 2022), as well as asymmetric chains under A-movement, consisting of a full copy and a pronominal $ \phi $-feature bundle; cf. similar configurations discussed by Takahashi & Hulsey (2009), Harizanov (2014), Kramer (2014), Baker & Kramer (2018), inter alios. It is shown that this approach accounts for the facts in Irish and that the same account can be extended to explain facts concerning participial agreement in, for example, Italian. Additional cross-linguistic implications are also considered, particularly with respect to French and Welsh.

The alignment of artificial intelligence (AI) systems with societal values and the public interest is a critical challenge in the field of AI ethics and governance. Traditional approaches, such as Reinforcement Learning with Human Feedback (RLHF) and Constitutional AI, often rely on pre-defined high-level ethical principles. This article critiques these conventional alignment frameworks through the philosophical perspectives of pragmatism and public interest theory, arguing against their rigidity and disconnect with practical impacts. It proposes an alternative alignment strategy that reverses the traditional logic, focusing on empirical evidence and the real-world effects of AI systems. By emphasizing practical outcomes and continuous adaptation, this pragmatic approach aims to ensure that AI technologies are developed according to the principles that are derived from the observable impacts produced by technology applications.

Does low mood have intentional content? If so, what is it? Philosophers have tried to answer both questions by appealing to low mood’s phenomenal character. However, appeals to phenomenology have not settled this debate. Thus, I take a different approach: I tackle both questions by examining low mood’s complex functional role in cognition. I argue that if we take this role into account, we have excellent reason to believe that low mood a) has content, and b) has the following indicative-imperative content: Good events are, on average, less likely to occur than bad events & Limit [the subject’s] resource expenditure!

This article is a preliminary discussion of the scientific value of archival lithics kept in museum collections and storage based on a small sample of Late Mesolithic flint artefacts from the site of Tomaszów II in south-eastern Poland, which was subjected to organic residue analysis. The aim of the trial study was to investigate and assess the preservation potential of organic residues on stone tools from sites located in areas not favourable to the survival of organic material and subsequently handled during post-excavation (especially those kept in museum collections). While the authors initially assumed that the chances of discovering residues indicating human use were slight and expected a general absence of organic material, the analysis of the lithics from Tomaszów II indicated that a small amount of ancient plant residues can survive on archival flint artefacts even in such unfavourable circumstances.

Guerini and Moneta (2017) have developed a sophisticated method of providing empirical evidence in support of the relations of causal dependence that macroeconomists engaging in agent-based modelling believe obtain in the target system of their models. The paper presents three problems that get in the way of successful applications of this method: problems that have to do with the potential chaos of the target system, the non-measurability of variables standing for individual or aggregate expectations, and the failure of macroeconomic aggregates to screen off individual expectations from the microeconomic quantities that constitute the aggregates. The paper also discusses the in-principle solvability of the three problems and uses a prominent agent-based model (the Keynes + Schumpeter model of the macroeconomy) as a running example.

All regimes require supporters to govern and survive. We discuss the concept of a ‘regime support group’ and present and validate measures from an extensive dataset recording different features of such groups. This Regime Support and Opposition Groups (ReSOG) dataset covers approximately 2,000 political regimes from almost 200 countries, across 1789–2020. Drawing on the knowledge of about 1,000 country experts, we estimate the size and main geographical location of regime support coalitions and key opposition actors. We also map the social basis of regime support coalitions and opponents, using a 14-category scheme covering various social groups. These data provide a unique quantitative history of the social underpinnings of regimes in the modern era. Using them, we show and discuss the broadening of support coalitions over time, especially in autocracies, the rise of major urban groups, and the relative decline of rural elites in politics, globally.

Galaxy cluster X-ray cavities are inflated by relativistic jets that are ejected into the intracluster medium by active galactic nuclei (AGN). AGN jets prevent predicted cooling flow establishment at the cluster centre, and while this process is not well understood in existing studies, simulations have shown that the heating mechanism will depend on the type of gas that fills the cavities. Thermal and non-thermal distributions of electrons will produce different cavity Sunyaev Zel’dovich (SZ) effect signals, quantified by the ‘suppression factor’ f. This paper explores potential enhancements to prior constraints on the cavity gas type by simulating suppression factor observations with the Square Kilometre Array (SKA). Cluster cavities across different redshifts are observed to predict the optimum way of measuring f in future observations. We find that the SKA can constrain the suppression factor in the cavities of cluster MS 0735.6+7421 (MS0735) in as little as 4 h, with a smallest observable value of $f \approx 0.42$. Additionally, while the SKA may distinguish between possible thermal or non-thermal suppression factor values within the cavities of MS0735 if it observes for more than 8 h, determining the gas type of other clusters will likely require observations at multiple frequencies. The effect of cavity line of sight (LOS) position is also studied, and degeneracies between LOS position and the measured value of f are found. Finally, we find that for small cavities (radius < 80 kpc) at high redshift ($z \approx 1.5$), the proposed high frequencies of the SKA (23.75–37.5 GHz) will be optimal, and that including MeerKAT antennas will improve all observations of this type.

Given a presentation of a monoid $M$, combined work of Pride and of Guba and Sapir provides an exact sequence connecting the relation bimodule of the presentation (in the sense of Ivanov) with the first homology of the Squier complex of the presentation, which is naturally a $\mathbb ZM$-bimodule. This exact sequence was used by Kobayashi and Otto to prove the equivalence of Pride’s finite homological type property with the homological finiteness condition bi-$\mathrm {FP}_3$. Guba and Sapir used this exact sequence to describe the abelianization of a diagram group. We prove here a generalization of this exact sequence of bimodules for presentations of associative algebras. Our proof is more elementary than the original proof for the special case of monoids.

This study shows the impact of black carbon (BC) aerosol atmospheric rivers (AAR) on the Antarctic Sea ice retreat. We detect that a higher number of BC AARs arrived in the Antarctic region due to increased anthropogenic wildfire activities in 2019 in the Amazon compared to 2018. Our analyses suggest that the BC AARs led to a reduction in the sea ice albedo, increased the amount of sunlight absorbed at the surface, and a significant reduction of sea ice over the Weddell, Ross Sea (Ross), and Indian Ocean (IO) regions in 2019. The Weddell region experienced the largest amount of sea ice retreat ($ \sim \mathrm{33,000} $ km2) during the presence of BC AARs as compared to $ \sim \mathrm{13,000} $ km2 during non-BC days. We used a suite of data science techniques, including random forest, elastic net regression, matrix profile, canonical correlations, and causal discovery analyses, to discover the effects and validate them. Random forest, elastic net regression, and causal discovery analyses show that the shortwave upward radiative flux or the reflected sunlight, temperature, and longwave upward energy from the earth are the most important features that affect sea ice extent. Canonical correlation analysis confirms that aerosol optical depth is negatively correlated with albedo, positively correlated with shortwave energy absorbed at the surface, and negatively correlated with Sea Ice Extent. The relationship is stronger in 2019 than in 2018. This study also employs the matrix profile and convolution operation of the Convolution Neural Network (CNN) to detect anomalous events in sea ice loss. These methods show that a higher amount of anomalous melting events were detected over the Weddell and Ross regions.

This article investigates marriage as a site for the historical study of time. Focusing on Hindu marriage in South Asia between the seventeenth and nineteenth centuries, the article studies (a) how the moment of a marriage is made and documented through what the article calls ‘temporal practices’, and (b) how, once this moment is made and documented, it is put to use in and for a marriage ceremony. The article has three sections. In the first section, it discusses the device used to measure the time of the marriage ceremony: the water clock. This section also addresses how the water clock was used, and who used it, within the marriage ceremony; and registers a shift in the nineteenth century from the water clock to the mechanical clock. In the second section, the article discusses documentary practices that record the moment of a marriage and addresses historical changes related to these practices in the nineteenth century. In the third section, the article examines the work that the moment of a marriage does once it has been brought into being and documented. This section argues that the moment of a marriage frames and makes efficacious a certain action through which the bride and groom are transformed. The article concludes by arguing that the moment of a marriage temporally regulates the activities of the marriage ceremony and explores how this moment reconfigures relations to the past and future for the bride and groom.

This study aimed to assess Well-Being Index scores in paediatric cardiac ICU (PCICU) registered nurses and advanced practice providers. Secondary objectives included identifying factors correlating with at-risk Well-Being Index scores and exploring predictors of these scores, with attention to the impact of the coronavirus disease 2019 pandemic. A multicentre electronic survey was conducted between October 2021 and January 2022. Registered nurses and advanced practice providers working in PCICUs at US centres participating in the Collaborative Research from the Pediatric Cardiac Intensive Care Society were included.

The survey included the nine-item Well-Being Index and questions about demographics and factors influencing well-being, such as coronavirus disease 2019. The Well-Being Index is a validated tool to predict workforce distress and well-being. Out of 218 participants (180 registered nurses, 38 advanced practice providers), 137 registered nurses (76%) and 15 advanced practice providers (39%) had at-risk Well-Being Index scores. A total of 61% of nurses and 34% of advanced practice providers reported an intent to leave. Intent to leave was significantly linked to lower well-being for registered nurses (p = 0.002). Leadership support reduced registered nurses’ distress risk by 68% compared to no support (p = 0.04). Increased stress since coronavirus disease 2019 raised registered nurses’ poor well-being risk by four times (p = 0.001). PCICU registered nurses and advanced practice providers are at risk for poor well-being, exacerbated by the pandemic. Those with poor well-being may be more likely to leave. Leadership support is vital for nurses’ well-being. Further research is needed to establish baseline well-being.